PPM-UNet: Uma Rede Neural Convolucional para a Segmentação de Rins em Imagens de TC

• Marcus Vinicius Oliveira UFMA
• Caio Eduardo Falcão Matos UFMA
• Geraldo Braz Júnior UFMA
• Anselmo Cardoso de Paiva UFMA
• João D. Sousa de Almeida UFMA
• Gabriel Costa UFMA
• Matheus Levy UFMA
• Mario Freitas UFMA

DOI: https://doi.org/10.5753/sbcas.2022.222656

Abstract

Kidney cancer represents 3% of adult malignancies, and early diagnosis is an essential tool. Automatic segmentation aims to assist the physician in the diagnostic process. The objective of this work is to develop and evaluate a network to segment, in computed tomography images, the regions of the kidneys and tumor, if any. We evaluated the use of Pyramid Pooling Module replacing convolutional layers of the U-Net network to achieve the goal. The proposed methodology achieves as a result 0.91 of Iou for kidney regions and 0.88 of Iou for tumor regions.

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